Gold nanoparticles (GNPs) as promising radiation sensitizers have been increasingly studied in a wide range of radiotherapy applications. By detecting the characteristic x-ray fluorescence (XRF) photons, x-ray fluorescence computed tomography (XFCT) can simultaneously determine both the spatial distribution and concentration of GNPs in vivo, affording for cancer diagnosis and irradiation guidance. However, the long scanning time of current single-pixel detectorbased configuration hinders the translation of XFCT to preclinical and clinical applications. This study presents a conebeam XFCT system using pixelated photon-counting detector with pinhole collimator to acquire XRF projection image in one motion, eliminating the previously step-by-step translation of objects, which allows fast whole-body GNP imaging. We have 3D printed a heat-resistance mold kit to cast a cone-beam x-ray source collimator using Cerrobend alloy. We selected HECITEC (High Energy X-ray Imaging Technology) as the XRF detector, in view of its high spatial resolution (0.25 mm of pitch) and energy resolution (800 eV FWHM at 60 keV). We have customized a 2-mm pinhole collimator to provide spatial information of XRF signals. We have also evaluated the roles of pixel binning and spectrum denoising in aspects of XRF peal extraction. Phantom experiments with GNP of different concentrations (0.078~2.5wt.%) were used to evaluate the sensitivity of GNP detection. In vivo experiments on mouse intravenously administered GNPs were used to validate the feasibility of the proposed system in terms of GNP biodistribution imaging. The results of this study will be helpful to guide XFCT development for routine in vivo GNP imaging
Gold nanoparticles (GNPs) are widely studied in medical research due to their favorable biocompatibility, variety in shape and size, and simple surface modification. Nanoparticles are particularly valuable in cancer research due to their enhanced permeation and retention effect property whereby nanoparticles accumulate in tumors. However, imaging GNPs are a challenge for most imaging modalities. Therefore, recent studies using x-ray fluorescence (XRF) imaging offer a potential for precise quantification and localization of GNPs without single endpoint studies such as immunohistochemistry and mass spectrometry. This study aims to accurately quantify and localize GNPs in ex-vivo tissue from GNP injected mice. 15-nm PEGylated GNPs were conjugated to anti-PSMA antibodies using 1-Ethyl-3-(3- dimethylaminopropyl) carbodiimide and N-hydroxy sulfosuccinimide. 3 SCID mice per group bearing subcutaneous LNCaP xenografts were intravenously injected with either anti-PSMA antibody conjugated GNPs (15mg/mL, 200μL) or Mouse IgG GNPs 24hrs prior to dissection. 11 organs along with the tumor were collected from the mice. Each organs’ GNP content was measured for quantification and localization via XRF on an in-house-developed dual-modality computed tomography and XRF system. Following imaging, organs were dehydrated and dissolved for quantification with inductively coupled plasma mass spectrometry analysis. XRF imaging quantified GNPs in tissue down to 25ng/g. Quantification with XRF imaging showed ~2x times greater accumulation of GNPs in the tumor with anti-PSMA targeted GNPs compared to Mouse IgG control GNPs. Additionally, XRF imaging of anti-PSMA targeted GNPs in all organs showed accurate quantification when compared to ICPMS analysis. XRF computed tomography further confirmed quantification of GNPs in tumors and spleen. This study confirmed the viability of XRF imaging for accurate quantification of anti-PSMA targeted GNPs in ex-vivo tissues.
KEYWORDS: Reconstruction algorithms, Luminescence, Signal to noise ratio, Tomography, In vivo imaging, Fluorescence tomography, 3D acquisition, 3D image processing, 3D image reconstruction, Inverse problems
Fluorescence molecular tomography (FMT) as a noninvasive imaging modality has been widely used for biomedical preclinical applications. However, FMT reconstruction suffers from severe ill-posedness, especially when a limited number of projections are used. In order to improve the quality of FMT reconstruction results, a discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm is proposed. In each iteration of the reconstruction process, different reweighted regularization parameters are adaptively assigned according to the values of DCT coefficients to suppress the reconstruction noise. In addition, the permission region of the reconstructed fluorophores is adaptively constructed to increase the convergence speed. In order to evaluate the performance of the proposed algorithm, physical phantom and in vivo mouse experiments with a limited number of projections are carried out. For comparison, different L1-norm regularization strategies are employed. By quantifying the signal-to-noise ratio (SNR) of the reconstruction results in the phantom and in vivo mouse experiments with four projections, the proposed DCT-based reweighted L1-norm regularization shows higher SNR than other L1-norm regularizations employed in this work.
KEYWORDS: Reconstruction algorithms, Luminescence, Tissues, Tomography, Spatial resolution, Fluorescence tomography, Detection and tracking algorithms, Signal attenuation, 3D acquisition, In vivo imaging
In fluorescence molecular tomography (FMT), the fluorophore distribution is reconstructed using the diffuse-light measurements obtained from the rotating source-detector pairs placed on the boundary of the tissues. Owing to the intensity attenuation of light when it propagates through tissues, the sensitivity of measurements deteriorates quickly with increased depth. Thus the inconsistent contrast of reconstructed fluorophores located at different depths is a major challenge in FMT. As a spatially variant regularization method, the adaptive support driven reweighted L1-minimization (ASDR-L1) algorithm is proposed here for depth compensation in FMT. ASDR-L1 is a modification of the restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm previously proposed by our laboratory. In ASDR-L1, the original L1-minimization problem is replaced by a sequence of weighted L1-minimization subproblems with spatially updated weights applied to the adaptive support estimate. Like re-L1-NCG, ASRDR-L1 adopts the restarted strategy in each outer iteration, which contributes to the adaptive support estimate. The updated weights for the next iteration spatially depend on the current solution. In the support estimate, spatially updated weights mean different regularization parameters for different locations. A large regularization parameter in the weighted L1-minimization subproblem makes the results concentrate on a small number of large values, whereas a small regularization parameter tends to make the values be evenly distributed. Thus depth compensation in FMT is achieved through the iteratively updated weights. Simulation experiments are conducted to confirm the feasibility of ASDR-L1. Through ASDR-L1, the reconstructed contrast between two identical fluorophores located at different depths is increased from 1:0.43 to 1:0.96.
Owing to the high degree of scattering of light through tissues, the ill-posedness of fluorescence molecular tomography (FMT) inverse problem causes relatively low spatial resolution in the reconstruction results. Unlike L2 regularization, L1 regularization can preserve the details and reduce the noise effectively. Reconstruction is obtained through a restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm, which has been proven to be able to increase the computational speed with low memory consumption. The algorithm consists of inner and outer iterations. In the inner iteration, L1-NCG is used to obtain the L1-regularized results. In the outer iteration, the restarted strategy is used to increase the convergence speed of L1-NCG. To demonstrate the performance of re-L1-NCG in terms of spatial resolution, simulation and physical phantom studies with fluorescent targets located with different edge-to-edge distances were carried out. The reconstruction results show that the re-L1-NCG algorithm has the ability to resolve targets with an edge-to-edge distance of 0.1 cm at a depth of 1.5 cm, which is a significant improvement for FMT.
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